Effect of Supervised Region of Interest Against Edge Detection Method for Iris Localisation

An Application Study of Improved Iris Image Localization Based on an Evolutionary Algorithm

“…∆τ ij represents the sum of pheromone left by m ant pairs on the path i~j, ∆τ k ij represents the pheromone of the kth ant on the path it I ~j traveled (Q represents the pheromone that an ant possesses in its lifetime), K represents the number of ants, m means the number of ants, and ρ denotes the degree of pheromone dilution (the ratio of the pheromone decreasing over time [32]). The formula in (18) will finally achieve the optimal solution of the fitness function and output the optimal path.…”

“…∆τ represents the sum of pheromone left by m ant pairs on the path i ~ j, ∆τ represents the pheromone of the kth ant on the path it i ~ j traveled (Q represents the pheromone that an ant possesses in its lifetime), K represents the number of ants, m means the number of ants, and ρ denotes the degree of pheromone dilution (the ratio of the pheromone decreasing over time [32]). The formula in (18) will finally achieve the optimal solution of the fitness function and output the optimal path. where P (t) is the tth generation of ants and the k ants choose to break into the east or go to the west of the probability; that is, the probability of ants k choosing to go from i ~ j, α on behalf of the degree of importance of the heuristic factor, β for the relative degree of importance of the heuristic factor, and n on behalf of the heuristic audience factor.…”

“…Bodade M. Rajesh et al [17] took advantage of the coupling between the inner and outer boundaries of the iris in order to reduce the search scope in the boundary parameter space and adopted the methods of "sampling first and then transforming" and "from coarse to fine". Zuraini Othman et al [18] proposed an iris localization method for ideal and non-ideal iris images. In this study, the algorithm determines all regions of interest (ROI) classifications using a support vector machine (SVM) and a histogram using gray levels as descriptors in all display growers.…”